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Estimating Tempo and Efficiency

It’s often useful to look at what those in the analytics community are doing for other sports to help you spark ideas on how to look at your own sport. A couple good examples that come to mind:

The introduction of Player Radar Charts over at Hockey-Graphs.com. I have been following the analytics community for soccer for some time and wanted to eventually try to develop these radars myself ever since Ted Knutson created them at com (As an aside, StatsBomb writers also follow the hockey analytics community since they’ve looked into topics such as PDO and Corsi for soccer).

The time Gabe Desjardins sent out a request for readers to manually track passes for a single game between the Red Wings and Blackhawks late in 2010. Theresults were a snapshot of what we ought to have in the hockey community, since they are so accessible in the soccer community.

What then if we look at hockey like the game of basketball? Every shot is a shot. Every turnover is a turnover. Every basket is a goal. Without a doubt, KemPom.com is a leader in the basketball analytics community, and the bulk of his research comes down to Tempo-Free stats. Instead of saying Virginia must have a crummy offense since they were 227nd out of 346 teams in terms of points per game, we should consider that they play a style that limits possessions. For every 100 possessions they got, they scored 118.5 points, good for 9th in the country. This is how efficient they were with their possessions, which is a much more reliable predictor of future success than simple points per game and also a better assessment of how good their offense played.

What if hockey teams act in a similar fashion? It’s easy to calculate goals per game, but what if some teams that appear to have tepid offenses are just playing a low-possession game? Conversely, what if we praise teams with a high amount of goals per game but play a fast paced aggressive brand of hockey that lets them see more opportunities? The problem then is in figuring out how many possessions occur in a hockey game.

I did related work in my chapter for Hockey Prospectus 2015-16. I looked at all of the event types in a game log and estimated based on the order of events which team had the puck. For instance, faceoff wins, takeaways, and giveaways all tell us which team has the puck following these events. All types of shots and hits tell me who had the puck directly before these events. This time around, instead of trying to see how long each team had the puck for, I wanted to see how many times the puck changed hands during the game.

I looked at last season’s regular season games and estimated how many times a team had the puck vs how many times the other team had the puck. These two numbers will always be off by one or exactly even in a basketball game, but in hockey they don’t need to be since teams can win more faceoffs or end a possession more often (e.g. having more shots on net that end in a goalie freezing it).

Highest Tempo Teams:

Team

Year

GP

Poss For

Poss Against

Avg Poss

Avg Poss/GP

MTL

2015-16

82

6240

6306

6273.0

76.5

TOR

2015-16

82

6282

6230

6256.0

76.3

PHI

2015-16

82

6241

6160

6200.5

75.6

OTT

2015-16

82

6170

6180

6175.0

75.3

BOS

2015-16

82

6162

6161

6161.5

75.1

L.A

2015-16

82

6225

6049

6137.0

74.8

NYI

2015-16

82

6099

6004

6051.5

73.8

EDM

2015-16

82

5998

6079

6038.5

73.6

PIT

2015-16

82

6024

6000

6012.0

73.3

DAL

2015-16

82

6065

5932

5998.5

73.2

ANA

2015-16

82

6027

5952

5989.5

73.0

WPG

2015-16

82

5868

5956

5912.0

72.1

WSH

2015-16

82

5864

5860

5862.0

71.5

NYR

2015-16

82

5828

5888

5858.0

71.4

CHI

2015-16

82

5788

5907

5847.5

71.3

S.J

2015-16

82

5842

5803

5822.5

71.0

FLA

2015-16

82

5786

5836

5811.0

70.9

STL

2015-16

82

5831

5758

5794.5

70.7

CBJ

2015-16

82

5736

5736

5736.0

70.0

CGY

2015-16

82

5723

5749

5736.0

70.0

NSH

2015-16

82

5691

5725

5708.0

69.6

ARI

2015-16

82

5757

5639

5698.0

69.5

T.B

2015-16

82

5690

5697

5693.5

69.4

VAN

2015-16

82

5483

5789

5636.0

68.7

CAR

2015-16

82

5739

5513

5626.0

68.6

BUF

2015-16

82

5522

5576

5549.0

67.7

COL

2015-16

82

5467

5549

5508.0

67.2

MIN

2015-16

82

5520

5398

5459.0

66.6

DET

2015-16

82

5438

5414

5426.0

66.2

N.J

2015-16

82

5251

5511

5381.0

65.6

Most Efficient Teams:

Team

Year

GP

Avg Poss/GP

GF

GA

GF/100 Poss

GA/100 Poss

GD/100 Poss

WSH

2015-16

82

71.5

252

193

4.3

3.3

1.0

PIT

2015-16

82

73.3

245

203

4.1

3.4

0.7

FLA

2015-16

82

70.9

239

203

4.1

3.5

0.7

DAL

2015-16

82

73.2

267

230

4.4

3.9

0.5

CHI

2015-16

82

71.3

235

209

4.1

3.5

0.5

S.J

2015-16

82

71.0

241

210

4.1

3.6

0.5

T.B

2015-16

82

69.4

227

201

4.0

3.5

0.5

ANA

2015-16

82

73.0

218

192

3.6

3.2

0.4

L.A

2015-16

82

74.8

225

195

3.6

3.2

0.4

NYR

2015-16

82

71.4

236

217

4.0

3.7

0.4

STL

2015-16

82

70.7

224

201

3.8

3.5

0.4

NSH

2015-16

82

69.6

228

215

4.0

3.8

0.3

NYI

2015-16

82

73.8

232

216

3.8

3.6

0.2

BOS

2015-16

82

75.1

240

230

3.9

3.7

0.2

MIN

2015-16

82

66.6

216

206

3.9

3.8

0.1

PHI

2015-16

82

75.6

214

218

3.4

3.5

-0.1

OTT

2015-16

82

75.3

236

247

3.8

4.0

-0.2

MTL

2015-16

82

76.5

221

236

3.5

3.7

-0.2

DET

2015-16

82

66.2

211

224

3.9

4.1

-0.3

N.J

2015-16

82

65.6

184

208

3.5

3.8

-0.3

BUF

2015-16

82

67.7

201

222

3.6

4.0

-0.3

WPG

2015-16

82

72.1

215

239

3.7

4.0

-0.3

COL

2015-16

82

67.2

216

240

4.0

4.3

-0.4

CGY

2015-16

82

70.0

231

260

4.0

4.5

-0.5

CBJ

2015-16

82

70.0

219

252

3.8

4.4

-0.6

EDM

2015-16

82

73.6

203

245

3.4

4.0

-0.6

CAR

2015-16

82

68.6

198

226

3.5

4.1

-0.6

VAN

2015-16

82

68.7

191

243

3.5

4.2

-0.7

ARI

2015-16

82

69.5

209

245

3.6

4.3

-0.7

TOR

2015-16

82

76.3

198

246

3.2

3.9

-0.8

In the Tempo table, we see Montreal leading the way. They were involved in games where the puck changed hands the most. They accomplished this feat a couple ways. They were 6th in the league in generating Corsi For events and 2nd in the league in Giveaways. I did a little more digging by looking at past years and conclude that this may be a change in playing style under Michel Therrien just for 2015-16, or just a fluky year since they were 12th in the league in 2013-14 and 13th in 2014-15 in terms of Avg Poss/GP.

We can confirm other trends like New Jersey hanging out in last place for the past three seasons. It’s no secret that they like to play “low event hockey” and this research further validates it. New Jersey and Minnesota, also known for their slower brand of hockey, were also in the bottom 3 in terms of Tempo for the past three seasons.

Looking at the Efficiency table, it’s no surprise to see Washington leading the way. They were 2nd in the league in GF/Poss and also 3rd in GA/Poss which means they were extremely efficient when they had the puck and extremely frustrating to try to score against when they didn’t have the puck. These numbers also tell us how a team plays. Pittsburgh and Dallas played a run n’ gun style of hockey since they could afford to. They scored much more often than they got scored on. Other teams like Tampa Bay and Nashville achieved success by slowing the game down while being more careful each time they had the puck.

This is a first pass for me at looking at hockey through the lens of another similar sport in basketball. There are things that sites like KenPom do that make their work more valuable to the game of basketball than this is to hockey. For instance, he adjusts his numbers based on which opponents a team plays since they play a much more lopsided schedule. Additionally, we ought to look at how predictive these numbers are in determining future success. These numbers mean a lot in basketball, but do they hold the same weight when translated to hockey? Lastly, we should look at instances when a high tempo team plays a low tempo team. Which sort of pace will the game take on?